For ImageNet, the total number of epochs is 100 with learning rate decay by 10 at milestones \(30, 60, 90\) . This is the standard approach for training Resnet-50 on ImageNet. For the method specific hyper-parameters we rely on existing experiments and their logical extensions. For LS, we use \(\epsilon =0.1\)  as utilized by [1]} and [2]}. For FL, we rely on using \(\gamma =3.0\)  as the authors utilized it for experiments on the Tiny-ImageNet [3]} dataset. For ERL, we use the strength to be \(0.1\)  based on the experiments of [2]}. We found that for TS the temperature \(1.1\)  provides reasonably well calibration. For MX, we employ \(\alpha =0.2\) .
